Finding Reliability of Slopes by Optimization of ANFIS with GA, FFA and PSO

The present study provides a novel strategy to find the reliability of soil slopes by optimizing ANFIS with GA, FFA and PSO. These three hybrid models are initialized with 206 datasets through MATLAB. The data sample is splitted in 30:70 for testing and training during model processing. The obtained model results are verified through regression plot, 36 statistical indices, Rank value, Taylor diagram and uncertainty analysis. R2 values found in regression for above mentioned models in training are 0.7624, 0.7011, and 0.7378 whereas in testing are 0.8142, 0.6720 and 0.7013 respectively. Some of the statistical errors such as Mean Square Error (MSE) values were 0.0148, 0.0182, 0.0159 in training and 0.1263, 0.0277 and 0.1289 in testing. Again the Root Mean Square Error (RMSE) values were found to be 0.1216, 0.1349, 0.1263 in training and 0.1256, 0.1664 and 0.1590 in testing. Furthermore, Mean Absolute Error (MAE) values were 0.0912, 0.0978, 0.0902 in training and 0.0968, 0.1283, and 0.1169 in testing. Such errors appear to have been close to zero. The total scores calculated for the hybrid models are 160,117, 141 and ranked the models as 1st, 3rd and 2nd, respectively. The results showed that ANFIS-GA is more efficient and accurate than the ANFIS-PSO model followed by ANFIS-FFA for computation of reliability in soil slopes. Furthermore, it may be suggested that ANFIS hybrid models might be a useful tool for solving slope stability problems.